Relationship between Thermal Conductivity and Compressive Strength of Insulation Concrete: A Review
Bibliographic record
Abstract
Developing insulation concrete with high strength is essential for the construction of energy saving buildings. This is important to achieve carbon neutrality in the modern building industry. This paper reviews the existing studies in the literature on insulation concrete. This paper aims to reveal the correlation between the thermal conductivity and strength of concrete and identify the most effective method to make insulation concrete with lower thermal conductivity but higher strength. The review is carried out from two perspectives, including the effects of different foaming methods and various lightweight aggregates. As for the foaming methods, the chemical and mechanical foaming methods are discussed. As for the lightweight aggregates, cenospheres, porous aggregates, aerogels, and phase change materials are assessed. It is clearly observed that the thermal conductivity and compressive strength of concrete can be fitted by a linear function. As for the foaming methods, chemical foaming using hydrogen peroxide is the most effective to produce concrete with relatively lower thermal conductivity and higher compressive strength. For concrete with lightweight aggregates, cenospheres are the best option. Finally, recommendations are made to develop concrete with lower thermal conductivity and higher strength.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".